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          Digital transformation fuels energy industry

          By SUN SHANGWU,TIAN XUEFEI and MA SI in Daqing, Heilongjiang | CHINA DAILY | Updated: 2022-08-25 07:42
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          A photovoltaic power project operates in Daqing. [Photo/Xinhua]

          Xu Liang, who leads a production team in the eighth operational area, said that after the system was launched, human supervision of oil and water wells was greatly reduced. The system immediately sends alerts when problems such as electrical equipment failures occur, Xu added.

          Wu Yi, general manager of the eighth operational area, said the internet of things system is part of a digital platform built at the plant with investment of nearly 12 million yuan ($1.77 million).

          This platform, which went online in January, is based on automatic data collection and analysis, radar-enabled real-time video monitoring, and patrol drones.

          This approach is in line with a report by United States consulting company McKinsey & Co, which states, "The key to unlocking full digital transformation across the oil and gas sector involves both soft and hard automation technologies, as well as requiring more nimble work practices."

          According to research by McKinsey, well construction and intervention typically account for 40 percent-and often as much as 70 percent-of an oil or gas company's capital spending. Furthermore, drilling and well activities are highly complex and take place in an environment with significant health and safety risks.

          "But emerging technologies are bringing significant opportunities to improve all aspects of the operating model for wells, from streamlining core processes to strengthening front-line capabilities, and are improving the overall organizational model," the McKinsey report said.

          Global energy companies are also using smart technologies for more-efficient production. For instance, to boost efficiency, US energy company ExxonMobil uses artificial intelligence technologies, from voice-activated virtual assistants to machine learning, to study and analyze vast quantities of data such as crude oil production rates.

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